Text Summarizer

Extract the key sentences from long passages — 100% on-device.

Runs locallyWorks offlineShare link carries settings, never your data
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Frequently asked questions

Is my document sent to an AI service?

No. Summarisation runs locally using an extractive ranking algorithm — no external AI API is called and nothing is uploaded. The tool scores each sentence by its relevance to the overall text using statistical analysis, then extracts the highest-ranking sentences to form a summary. This approach keeps sensitive company documents, confidential reports, and personal information completely private and on your device. Because no cloud API is involved, summarization works offline after the page loads and requires no authentication or account login.

How does the summary choose sentences?

It scores each sentence by how central it is to the overall text and keeps the highest-ranked ones, preserving the original wording. The scoring algorithm analyzes word frequency, sentence position, and relationship to the main topics to identify the most important sentences without rewriting. This extractive approach means you always get verbatim text from the original document, which preserves the author's voice and ensures accuracy for factual documents. You can adjust how much of the original document to keep (e.g., 25%, 50%, 75%) to get a shorter or longer summary.

Is it free?

Completely. There is no sign-up, no account, no watermark and no usage limit. The tool is supported by unobtrusive ads, not by selling or processing your data.

Pro tips

  • Strip navigation, cookie notices and boilerplate before summarising. Repeated furniture competes with the actual content for ranking.
  • Start at three or four sentences and increase — it is easier to notice a missing point than to spot padding in an over-long summary.
  • Read the selected sentences in order against the original's structure; if they all come from the opening, the piece probably buried its conclusion.
  • Use it as a triage step for deciding what to read properly, not as a replacement for reading anything you are accountable for.
  • Prefer it over a generative summariser whenever faithfulness matters more than readability, since nothing here can be paraphrased into something the source did not claim.

About Text Summarizer

The summary is extractive: it selects sentences that already exist rather than writing new ones, so nothing is invented or paraphrased. The wording stays faithful to the source, but a rambling original still produces a rambling summary.

Paste a long article, report or document and extract its most important sentences into a concise summary. It ranks sentences by relevance using an on-device algorithm, giving you a quick TL;DR without reading every paragraph.

No AI service is involved, so there is no cost, no rate limit and nothing queued behind other people's requests. Adjust how many sentences to keep and copy the result in one click.

Extractive summarising has one property that matters more than fluency: it cannot invent. Every sentence in the output appeared verbatim in the input, so the summary can be wrong about emphasis but never about fact. A generative summary reads better and can state something the source never said, which is the wrong failure mode for a contract, a specification or a medical letter.

What it handles poorly is worth knowing before you rely on it. An argument spread thinly across many sentences has no single sentence to select, so it will be under-represented; dialogue and heavily structured documents score badly because sentence boundaries stop matching meaning; and a summary of a rambling original is a shorter ramble, because there was never a well-formed sentence carrying the point.

Sentence length skews any extractive ranking, which is worth knowing when reading the output. Longer sentences contain more scoring terms and are therefore more likely to be selected, so a document written in short declarative sentences can be under-represented relative to one padded with subordinate clauses. If a summary feels like it favoured the wordiest parts of a piece, that is the mechanism rather than a judgement about importance.

Common use cases

  • Researchers triaging a stack of papers or reports to decide which few deserve a full read.
  • Students condensing a long chapter into the handful of sentences that carry the argument.
  • Professionals extracting the operative points from a long email thread or meeting transcript.
  • Analysts skimming lengthy filings or policy documents where an invented sentence would be worse than a clumsy one.
  • Anyone handling text they are not permitted to paste into a third-party AI service.
How it comparesA large language model writes a far more readable summary and can misstate the source while doing it. This trades that fluency for a guarantee: every sentence is one the author actually wrote, with no API key, no queue and no copy of your document sitting in someone's logs.